Computer chess

Computer chess IC bearing the name of developer Frans Morsch (see Mephisto)

Chess machines/programs are available in several different forms: as stand-alone chess machines (usually a microprocessor running a software chess program, but sometimes as a specialized hardware machine), software programs running on standard PCs, web sites, and apps for mobile devices. Programs run on everything from super-computers to smartphones. Hardware requirements for programs are minimal: the apps are no larger than a few megabytes on disk, use a few megabytes of memory (but can use much more, if it is available), and any processor 300Mhz or faster is sufficient. Performance will vary modestly with processor speed, but sufficient memory to hold a large transposition table (up to several gigabytes or more) is more important to playing strength than processor speed.

Most available commercial chess programs and machines are super-grandmaster playing strength (ELO 2700 or more), and take advantage of multi-core and hyperthreaded computer CPU architectures. Top programs such as Stockfish have surpassed even world champion caliber players. Most chess engines interface to a GUI like Winboard or Chessbase and playing strength, time controls, and other performance-related settings are adjustable from the GUI. Most GUIs also allow the player to set up and edit positions, take back moves, offer and accept draws (and resign), have a "coach" function to recommend a move when the player is in doubt, and show the engine's analysis as the game progresses.

There are a few chess engines such as Sargon, IPPOLIT, Stockfish, Crafty, Fruit and GNU Chess that can be downloaded (or source code otherwise obtained) from the Internet free of charge.

Perhaps the most common type of chess software are programs that simply play chess. You make a move on the board, and the AI calculates and plays a response, and back and forth until one player resigns. Sometimes the chess engine, which calculates the moves, and the graphical user interface (GUI) are separate programs. A variety of engines can be imported into the GUI, so that you can play against different styles. Engines often have just a simple text command-line interface while GUIs may offer a variety of piece sets, board styles or even 3D or animated pieces. Because recent engines are so strong, engines or GUIs may offer some way of limiting the engine's strength, so the player has a better chance of winning. Universal Chess Interface (UCI) engines such Fritz or Rybka may have a built in mechanism for reducing the Elo rating of the engine (via UCI's uci_limitstrength and uci_elo parameters). Some versions of Fritz have a Handicap and Fun mode for limiting the current engine or changing the percentage of mistakes it makes or changing its style. Fritz also has a Friend Mode where during the game it tries to match the level of the player.

Chess databases allow users to search through a large library of historical games, analyze them, check statistics, and draw up an opening repertoire. Chessbase (for PC) is perhaps the most common program for this amongst professional players, but there are alternatives such as Shane's Chess Information Database (Scid) [2] for Windows, Mac or Linux, Chess Assistant[3] for PC,[4] Gerhard Kalab's Chess PGN Master for Android[5] or Giordano Vicoli's Chess-Studio for iOS.[6]

Programs such as Playchess allow you to play games against other players over the internet.

Chess training programs teach chess. Chessmaster had playthrough tutorials by IM Josh Waitzkin and GM Larry Christiansen. Stefan Meyer-Kahlen offers Shredder Chess Tutor based on the Step coursebooks of Rob Brunia and Cor Van Wijgerden. World champions Magnus Carlsen's Play Magnus company recently released a Magnus Trainer app for Android and iOS. Chessbase has Fritz and Chesster for children. Convekta has a large number of training apps such as CT-ART and its Chess King line based on tutorials by GM Alexander Kalinin and Maxim Blokh.

There is also Software for handling chess problems.

After discovering refutation screening—the application of alpha-beta pruning to optimizing move evaluation—in 1957, a team at Carnegie Mellon University predicted that a computer would defeat the world human champion by 1967.[7] It did not anticipate the difficulty of determining the right order to evaluate moves. Researchers worked to improve programs' ability to identify killer heuristics, unusually high-scoring moves to reexamine when evaluating other branches, but into the 1970s most top chess players believed that computers would not soon be able to play at a Master level.[8] In 1968 International Master David Levy made a famous bet that no chess computer would be able to beat him within ten years,[9] and in 1976 Senior Master and professor of psychology Eliot Hearst of Indiana University wrote that "the only way a current computer program could ever win a single game against a master player would be for the master, perhaps in a drunken stupor while playing 50 games simultaneously, to commit some once-in-a-year blunder".[8]

In the late 1970s chess programs suddenly began defeating top human players.[8] The year of Hearst's statement, Northwestern University's Chess 4.5 at the Paul Masson American Chess Championship's Class B level became the first to win a human tournament. Levy won his bet in 1978 by beating Chess 4.7, but it achieved the first computer victory against a Master-class player at the tournament level by winning one of the six games.[9] In 1980 Belle began often defeating Masters. By 1982 two programs played at Master level and three were slightly weaker.[8]

The sudden improvement without a theoretical breakthrough surprised humans, who did not expect that Belle's ability to examine 100,000 positions a second—about eight plies—would be sufficient. The Spracklens, creators of the successful microcomputer program Sargon, estimated that 90% of the improvement came from faster evaluation speed and only 10% from improved evaluations. New Scientist stated in 1982 that computers "play terrible chess ... clumsy, inefficient, diffuse, and just plain ugly", but humans lost to them by making "horrible blunders, astonishing lapses, incomprehensible oversights, gross miscalculations, and the like" much more often than they realized; "in short, computers win primarily through their ability to find and exploit miscalculations in human initiatives".[8]

By 1982, microcomputer chess programs could evaluate up to 1,500 moves a second and were as strong as mainframe chess programs of five years earlier, able to defeat almost all players. While only able to look ahead one or two plies more than at their debut in the mid-1970s, doing so improved their play more than experts expected; seemingly minor improvements "appear to have allowed the crossing of a psychological threshold, after which a rich harvest of human error becomes accessible", New Scientist wrote.[8] While reviewing SPOC in 1984, BYTE wrote that "Computers—mainframes, minis, and micros—tend to play ugly, inelegant chess", but noted Robert Byrne's statement that "tactically they are freer from error than the average human player". The magazine described SPOC as a "state-of-the-art chess program" for the IBM PC with a "surprisingly high" level of play, and estimated its USCF rating as 1700 (Class B).[10]

At the 1982 North American Computer Chess Championship, Monroe Newborn predicted that a chess program could become world champion within five years; tournament director and International Master Michael Valvo predicted ten years; the Spracklens predicted 15; Ken Thompson predicted more than 20; and others predicted that it would never happen. The most widely held opinion, however, stated that it would occur around the year 2000.[11] In 1989, Levy was defeated by Deep Thought in an exhibition match. Deep Thought, however, was still considerably below World Championship Level, as the then reigning world champion Garry Kasparov demonstrated in two strong wins in 1989. It was not until a 1996 match with IBM's Deep Blue that Kasparov lost his first game to a computer at tournament time controls in Deep Blue - Kasparov, 1996, Game 1. This game was, in fact, the first time a reigning world champion had lost to a computer using regular time controls. However, Kasparov regrouped to win three and draw two of the remaining five games of the match, for a convincing victory.

In May 1997, an updated version of Deep Blue defeated Kasparov 3½–2½ in a return match. A documentary mainly about the confrontation was made in 2003, titled Game Over: Kasparov and the Machine. IBM keeps a web site of the event.

Deep Blue vs. Kasparov, 1996, game 1

	a	b	c	d	e	f	g	h
8									8
7									7
6									6
5									5
4									4
3									3
2									2
1									1
	a	b	c	d	e	f	g	h

Final position

With increasing processing power and improved evaluation functions, chess programs running on commercially available workstations began to rival top flight players. In 1998, Rebel 10 defeated Viswanathan Anand, who at the time was ranked second in the world, by a score of 5–3. However, most of those games were not played at normal time controls. Out of the eight games, four were blitz games (five minutes plus five seconds Fischer delay (see time control) for each move); these Rebel won 3–1. Two were semi-blitz games (fifteen minutes for each side) that Rebel won as well (1½–½). Finally, two games were played as regular tournament games (forty moves in two hours, one hour sudden death); here it was Anand who won ½–1½.[12] In fast games, computers played better than humans, but at classical time controls – at which a player's rating is determined – the advantage was not so clear.

In the early 2000s, commercially available programs such as Junior and Fritz were able to draw matches against former world champion Garry Kasparov and classical world champion Vladimir Kramnik.

In October 2002, Vladimir Kramnik and Deep Fritz competed in the eight-game Brains in Bahrain match, which ended in a draw. Kramnik won games 2 and 3 by "conventional" anti-computer tactics – play conservatively for a long-term advantage the computer is not able to see in its game tree search. Fritz, however, won game 5 after a severe blunder by Kramnik. Game 6 was described by the tournament commentators as "spectacular." Kramnik, in a better position in the early middlegame, tried a piece sacrifice to achieve a strong tactical attack, a strategy known to be highly risky against computers who are at their strongest defending against such attacks. True to form, Fritz found a watertight defense and Kramnik's attack petered out leaving him in a bad position. Kramnik resigned the game, believing the position lost. However, post-game human and computer analysis has shown that the Fritz program was unlikely to have been able to force a win and Kramnik effectively sacrificed a drawn position. The final two games were draws. Given the circumstances, most commentators still rate Kramnik the stronger player in the match.

In January 2003, Garry Kasparov played Junior, another chess computer program, in New York City. The match ended 3–3.

In November 2003, Garry Kasparov played X3D Fritz. The match ended 2–2.

In 2005, Hydra, a dedicated chess computer with custom hardware and sixty-four processors and also winner of the 14th IPCCC in 2005, defeated seventh-ranked Michael Adams 5½–½ in a six-game match (though Adams' preparation was far less thorough than Kramnik's for the 2002 series).[13]

In November–December 2006, World Champion Vladimir Kramnik played Deep Fritz. This time the computer won; the match ended 2–4. Kramnik was able to view the computer's opening book. In the first five games Kramnik steered the game into a typical "anti-computer" positional contest. He lost one game (overlooking a mate in one), and drew the next four. In the final game, in an attempt to draw the match, Kramnik played the more aggressive Sicilian Defence and was crushed.

There was speculation that interest in human-computer chess competition would plummet as a result of the 2006 Kramnik-Deep Fritz match.[14] According to Newborn, for example, "the science is done".[15]

Human-computer chess matches showed the best computer systems overtaking human chess champions in the late 1990s. For the 40 years prior to that, the trend had been that the best machines gained about 40 points per year in the Elo rating while the best humans only gained roughly 2 points per year.[16] The highest rating obtained by a computer in human competition was Deep Thought's USCF rating of 2551 in 1988 and FIDE no longer accepts human-computer results in their rating lists. Specialized machine-only Elo pools have been created for rating machines, but such numbers, while similar in appearance, should not be directly compared.[17] In 2016, the Swedish Chess Computer Association rated computer program Komodo at 3361.

Chess engines continue to improve. In 2009, chess engines running on slower hardware have reached the grandmaster level. A mobile phone won a category 6 tournament with a performance rating 2898: chess engine Hiarcs 13 running inside Pocket Fritz 4 on the mobile phone HTC Touch HD won the Copa Mercosur tournament in Buenos Aires, Argentina with 9 wins and 1 draw on August 4–14, 2009.[18] Pocket Fritz 4 searches fewer than 20,000 positions per second.[19] This is in contrast to supercomputers such as Deep Blue that searched 200 million positions per second.

Advanced Chess is a form of chess developed in 1998 by Kasparov where a human plays against another human, and both have access to computers to enhance their strength. The resulting "advanced" player was argued by Kasparov to be stronger than a human or computer alone, this has been proven in numerous occasions, at Freestyle Chess events.

Players today are inclined to treat chess engines as analysis tools rather than opponents.[20] Chess grandmaster Andrew Soltis stated in 2016 "The computers are just much too good" and that world champion Magnus Carlsen won't play computer chess because "he just loses all the time and there's nothing more depressing than losing without even being in the game."[21]

Since the era of mechanical machines that played rook and king endings and electrical machines that played other games like hex (game) in the early years of the 20th century, scientists and theoreticians have sought to develop a procedural representation of how humans learn, remember, think and apply knowledge, and the game of chess, because of its daunting complexity, became the "Drosophila of artificial intelligence (AI)".[22] The procedural resolution of complexity became synonymous with thinking, and early computers, even before the chess automaton era, were popularly referred to as "electronic brains". Several different schema were devised starting in the latter half of the 20th century to represent knowledge and thinking, as applied to playing the game of chess (and other games like checkers):

• search based (minimax/alphabeta or selective search)
• knowledge based (PARADISE)
• statistical sampling (monte-carlo tree searching)
• genetic algorithms
• machine learning

Using "ends-and-means" heuristics a human chess player can intuitively determine optimal outcomes and how to achieve them regardless of the number of moves necessary, but a computer must be systematic in its analysis. Most players agree that looking at least five moves ahead (ten plies) when necessary is required to play well. Normal tournament rules give each player an average of three minutes per move. On average there are more than 30 legal moves per chess position, so a computer must examine a quadrillion possibilities to look ahead ten plies (five full moves); one that could examine a million positions a second would require more than 30 years.[8]

The earliest attempts at procedural representations of playing chess predated the digital electronic age, but it was the stored program digital computer that gave scope to calculating such complexity. Claude Shannon, in 1949, laid out the principles of algorithmic solution of chess. In that paper, the game is represented by a "tree", or digital data structure of choices (branches) corresponding to moves. The nodes of the tree were positions on the board resulting from the choices of move. The impossibility of representing an entire game of chess by constructing a tree from first move to last was immediately apparent: there are an average of 36 moves per position in chess and an average game lasts about 35 moves to resignation (60-80 moves if played to checkmate, stalemate, or other draw). There are 400 positions possible after the first move by each player, about 200,000 after two moves each, and nearly 120 million after just 3 moves each. So a limited lookahead (search) to a fixed depth, followed by using domain-specific knowledge to evaluate the resulting terminal positions was proposed. A kind of middle-ground (later referred to as a "minimax") position, given good moves by both sides, would result, and its evaluation would inform the player about the goodness or badness of the moves chosen. Searching and comparing operations on the tree were well suited to computer calculation; the representation of subtle chess knowledge in the evaluation function was not. The early chess programs suffered in both areas: searching the vast tree required computational resources far beyond those available, and what chess knowledge was useful and how it was to be encoded would take decades to discover.

An early search paradigm called alpha–beta pruning, a system of defining upper and lower bounds on possible search results and searching until the bounds coincided, reduced the branching factor of the game tree logarithmically, but it still was not feasible for chess programs to exploit the exponential explosion of the tree. This led naturally to what is referred to as "selective search", using chess knowledge (heuristics) to select a few presumably good moves from each position to search, and prune away the others without searching. But chess is not a game given to topical inspection, and the goodness or badness of a move may not be determined for many moves into the game, so selective search often resulted in the best move or moves being pruned away. Little or no progress was made for the next 25 years dominated by the selective search paradigm. The best program produced during this time was Mac Hack VI in 1967; it played at the about the same level as the average amateur (C class on the United States Chess Federation rating scale).

In 1974, another search paradigm was implemented for the first time in the Northwestern University Chess 4.0 program, the alternative described in Shannon's 1949 paper, called full-width or "brute force" searching. In this approach, all alternative moves at a node are searched, and none are pruned away. They discovered that the time required to simply search all the moves was much less than the time required to apply knowledge-intensive heuristics to select just a few of them, and the benefit of not prematurely or inadvertently pruning away good moves resulted in substantially stronger performance.

The developers of a chess-playing computer system must decide on a number of fundamental implementation issues. These include:

• Graphical user interface (GUI) - how moves are entered and communicated to the user, how the game is recorded, how the time controls are set, and other interface considerations
• Board representation – how a single position is represented in data structures;
• Search techniques – how to identify the possible moves and select the most promising ones for further examination;
• Leaf evaluation – how to evaluate the value of a board position, if no further search will be done from that position.

Endgame play had long been one of the great weaknesses of chess programs, because of the depth of search needed. Some otherwise master-level programs were unable to win in positions where even intermediate human players can force a win.

To solve this problem, computers have been used to analyze some chess endgame positions completely, starting with king and pawn against king. Such endgame tablebases are generated in advance using a form of retrograde analysis, starting with positions where the final result is known (e.g., where one side has been mated) and seeing which other positions are one move away from them, then which are one move from those, etc. Ken Thompson was a pioneer in this area.

The results of the computer analysis sometimes surprised people. In 1977 Thompson's Belle chess machine used the endgame tablebase for a king and rook against king and queen and was able to draw that theoretically lost ending against several masters (see Philidor position#Queen versus rook). This was despite not following the usual strategy to delay defeat by keeping the defending king and rook close together for as long as possible. Asked to explain the reasons behind some of the program's moves, Thompson was unable to do so beyond saying the program's database simply returned the best moves.

Most grandmasters declined to play against the computer in the queen versus rook endgame, but Walter Browne accepted the challenge. A queen versus rook position was set up in which the queen can win in thirty moves, with perfect play. Browne was allowed 2½ hours to play fifty moves, otherwise a draw would be claimed under the fifty-move rule. After forty-five moves, Browne agreed to a draw, being unable to force checkmate or win the rook within the next five moves. In the final position, Browne was still seventeen moves away from checkmate, but not quite that far away from winning the rook. Browne studied the endgame, and played the computer again a week later in a different position in which the queen can win in thirty moves. This time, he captured the rook on the fiftieth move, giving him a winning position (Levy & Newborn 1991:144–48), (Nunn 2002:49).

Other positions, long believed to be won, turned out to take more moves against perfect play to actually win than were allowed by chess's fifty-move rule. As a consequence, for some years the official FIDE rules of chess were changed to extend the number of moves allowed in these endings. After a while, the rule reverted to fifty moves in all positions — more such positions were discovered, complicating the rule still further, and it made no difference in human play, as they could not play the positions perfectly.

Over the years, other endgame database formats have been released including the Edward Tablebase, the De Koning Database and the Nalimov Tablebase which is used by many chess programs such as Rybka, Shredder and Fritz. Tablebases for all positions with six pieces are available.[25] Some seven-piece endgames have been analyzed by Marc Bourzutschky and Yakov Konoval.[26] Programmers using the Lomonosov supercomputers in Moscow have completed a chess tablebase for all endgames with seven pieces or fewer (trivial endgame positions are excluded, such as six white pieces versus a lone black king).[27][28] In all of these endgame databases it is assumed that castling is no longer possible.

Many tablebases do not consider the fifty-move rule, under which a game where fifty moves pass without a capture or pawn move can be claimed to be a draw by either player. This results in the tablebase returning results such as "Forced mate in sixty-six moves" in some positions which would actually be drawn because of the fifty-move rule. One reason for this is that if the rules of chess were to be changed once more, giving more time to win such positions, it will not be necessary to regenerate all the tablebases. It is also very easy for the program using the tablebases to notice and take account of this 'feature' and in any case if using an endgame tablebase will choose the move that leads to the quickest win (even if it would fall foul of the fifty-move rule with perfect play). If playing an opponent not using a tablebase, such a choice will give good chances of winning within fifty moves.

The Nalimov tablebases, which use state-of-the-art compression techniques, require 7.05 GB of hard disk space for all five-piece endings. To cover all the six-piece endings requires approximately 1.2 TB. It is estimated that a seven-piece tablebase requires between 50 and 200 TB of storage space.[29]

Endgame databases featured prominently in 1999, when Kasparov played an exhibition match on the Internet against the rest of the world. A seven piece Queen and pawn endgame was reached with the World Team fighting to salvage a draw. Eugene Nalimov helped by generating the six piece ending tablebase where both sides had two Queens which was used heavily to aid analysis by both sides.

Chess engines, like human beings, may save processing time as well as select strong variations as expounded by the masters, by referencing an opening book stored in a disk database. Opening books cover the opening moves of a game to variable depth, depending on opening and variation, but usually to the first 10-12 moves (20-24 ply). Since the openings have been studied in depth by the masters for centuries, and some are known to well into the middle game, the valuations of specific variations by the masters will usually be superior to the general heuristics of the program.

While at one time, playing an out-of-book move in order to put the chess program onto its own resources might have been an effective strategy because chess opening books were selective to the program's playing style, and programs had notable weaknesses relative to humans, that is no longer true today. The opening books stored in computer databases are most likely far more extensive than even the best prepared humans, and playing an early out-of-book move may result in the computer finding the unusual move in its book and saddling the opponent with a sharp disadvantage. Even if it does not, playing out-of-book may be much better for tactically sharp chess programs than for humans who have to discover strong moves in an unfamiliar variation over the board.

CEGT,[30] CSS,[31] SSDF,[32] and WBEC[33] maintain rating lists allowing fans to compare the strength of engines. Various versions of Stockfish, Komodo and Houdini dominate the IPON rating list in the late 2010s.

CCRL (Computer Chess Rating Lists) is an organisation that tests computer chess engines' strength by playing the programs against each other. CCRL was founded in 2006 to promote computer-computer competition and tabulate results on a rating list.[34]

The organisation runs three different lists: 40/40 (40 minutes for every 40 moves played), 40/4 (4 minutes for every 40 moves played), and 40/4 FRC (same time control but Chess960).[Note 1] Pondering (or permanent brain) is switched off and timing is adjusted to the AMD64 X2 4600+ (2.4 GHz) CPU by using Crafty 19.17 BH as a benchmark. Generic, neutral opening books are used (as opposed to the engine's own book) up to a limit of 12 moves into the game alongside 4 or 5 man tablebases.[34][35][36]

Well-known computer chess theorists include:

• Georgy Adelson-Velsky, a Soviet and Israeli mathematician and computer scientist
• Hans Berliner, American computer scientist and world correspondence chess champion, design supervisor of HiTech (1988)
• Mikhail Botvinnik, Soviet electrical engineer and world chess champion, wrote Pioneer
• Alexander Brudno, Russian computer scientist, first elaborated the alphabeta pruning algorithm
• Feng-hsiung Hsu, the lead developer of Deep Blue (1986–97)
• Professor Robert Hyatt developed Cray Blitz and Crafty[60]
• Danny Kopec, American Professor or Computer Science and International Chess Master, developed Kopec-Bratko test
• Alexander Kronrod, Soviet computer scientist and mathematician
• Professor Monroe Newborn, chairman of the computer chess committee for the Association of Computing Machinery
• Claude E. Shannon, American computer scientist and mathematician
• Alan Turing, English computer scientist and mathematician

The prospects of completely solving chess are generally considered to be rather remote. It is widely conjectured that there is no computationally inexpensive method to solve chess even in the very weak sense of determining with certainty the value of the initial position, and hence the idea of solving chess in the stronger sense of obtaining a practically usable description of a strategy for perfect play for either side seems unrealistic today. However, it has not been proven that no computationally cheap way of determining the best move in a chess position exists, nor even that a traditional alpha-beta searcher running on present-day computing hardware could not solve the initial position in an acceptable amount of time. The difficulty in proving the latter lies in the fact that, while the number of board positions that could happen in the course of a chess game is huge (on the order of at least 10⁴³[61] to 10⁴⁷), it is hard to rule out with mathematical certainty the possibility that the initial position allows either side to force a mate or a threefold repetition after relatively few moves, in which case the search tree might encompass only a very small subset of the set of possible positions. It has been mathematically proven that generalized chess (chess played with an arbitrarily large number of pieces on an arbitrarily large chessboard) is EXPTIME-complete,[62] meaning that determining the winning side in an arbitrary position of generalized chess provably takes exponential time in the worst case; however, this theoretical result gives no lower bound on the amount of work required to solve ordinary 8x8 chess.

Martin Gardner's Minichess, played on a 5×5 board with approximately 10¹⁸ possible board positions, has been solved; its game-theoretic value is 1/2 (i.e. a draw can be forced by either side), and the forcing strategy to achieve that result has been described.

Progress has also been made from the other side: as of 2012, all 7 and fewer pieces (2 kings and up to 5 other pieces) endgames have been solved.

A "chess engine" is software that calculates and orders which moves are the strongest to play in a given position. Engine authors focus on improving the play of their engines, often just importing the engine into a graphical user interface (GUI) developed by someone else. Engines communicate with the GUI by following standardized protocols such as the Universal Chess Interface developed by Stefan Meyer-Kahlen and Franz Huber or the Chess Engine Communication Protocol developed by Tim Mann for GNU Chess and Winboard. Chessbase has its own proprietary protocol, and at one time Millennium 2000 had another protocol used for ChessGenius. Engines designed for one operating system and protocol may be ported to other OS's or protocols.

In 1997, the Internet Chess Club released its first Java client for playing chess online against other people inside one's webbrowser.[63] This was probably one of the first chess web apps. Free Internet Chess Server followed soon after with a similar client.[64] In 2004, International Correspondence Chess Federation opened up a web server to replace their email based system.[65] Chess.com started offering Live Chess in 2007.[66] Chessbase/Playchess had long had a downloadable client, but they had a web interface by 2013.[67]

Another popular web app is tactics training. The now defunct Chess Tactics Server opened its site in 2006,[68] followed by Chesstempo the next year,[69] and Chess.com added its Tactics Trainer in 2008.[70] Chessbase added a tactics trainer web app in 2015.[71]

Chessbase took their chess game database online in 1998.[72] Another early chess game databases was Chess Lab, which started in 1999.[73] New In Chess had initially tried to compete with Chessbase by releasing a NICBase program for Windows 3.x, but eventually, decided to give up on software, and instead focus on their online database starting in 2002.[74]

One could play against the engine Shredder online from 2006.[75] In 2015, Chessbase added a play Fritz web app,[76] as well as My Games for storing one's games.[77]

Starting in 2007, Chess.com offered the content of the training program, Chess Mentor, to their customers online.[78] Top GMs such as Sam Shankland and Walter Browne have contributed lessons.

• Computer Go
• Computer Othello
• Computer shogi

• Hsu, Feng-hsiung (2002), Behind Deep Blue: Building the Computer that Defeated the World Chess Champion, Princeton University Press, ISBN 0-691-09065-3
• Levy, David; Newborn, Monty (1991), How Computers Play Chess, Computer Science Press, ISBN 0-7167-8121-2
• Newborn, Monty (1975), Computer Chess, Academic Press, New York
• Newborn, Monty (1997), Kasparov versus Deep Blue: Computer Chess Comes of Age, Springer, ISBN 0-387-94820-1 (This book actually covers computer chess from the early days through the first match between Deep Blue and Garry Kasparov.)
• Nunn, John (2002), Secrets of Pawnless Endings, Gambit Publications, ISBN 1-901983-65-X
• Shannon, Claude E. (1950), "Programming a Computer for Playing Chess" (PDF), Philosophical Magazine, Ser.7, Vol. 41 (314), archived from the original (PDF) on 6 July 2010, retrieved 21 June 2009
• Mastering the Game: A History of Computer Chess at Computer History Museum
• Bill Wall's Computer Chess History Timeline

• New Architectures in Computer Chess – Thesis on How to Build A Chess Engine
• Coles, L. Stephen (October 30, 2002), Computer Chess: The Drosophila of AI, Dr. Dobb's Journal
• Huberman (Liskov), Barbara Jane (1968), A program to play chess end games, Stanford University Department of Computer Science, Technical Report CS 106, Stanford Artificial Intelligence Project Memo AI-65
• Lasar, Matthew (2011). Brute force or intelligence? The slow rise of computer chess". Ars Technica.
• Newborn, Monty (1996). Outsearching Kasparov, American Mathematical Society's Proceeding of Symposia in Applied Mathematics: Mathematical Aspects of Artificial Intelligence, v. 55, pp 175–205, 1998. Based on paper presented at the 1996 Winter Meeting of the AMS, Orlando, Florida, Jan 9–11, 1996.
• Newborn, Monty (2000). Deep Blue's contribution to AI, Annals of Mathematics and Artificial Intelligence, v. 28, pp. 27–30, 2000.
• Newborn, Monty (2006). Theo and Octopus at the 2006 World Championship for Automated Reasoning Programs, Seattle, Washington, August 18, 2006
• Stiller, Lewis (1996), Multilinear Algebra and Chess Endgames (PDF), Berkeley, California: Mathematical Sciences Research Institute, Games of No Chance, MSRI Publications, Volume 29, retrieved 21 June 2009

Look up computer chess in Wiktionary, the free dictionary.

Wikimedia Commons has media related to Chess computers.

• List of chess engine ratings and game files in PGN format
• Mastering the Game: A History of Computer Chess at the Computer History Museum
• ACM Computer Chess by Bill Wall
• Computer Chess Information and Resources – Blog following the creation of a computer chess engine
• Defending Humanity's Honor, an article by Tim Krabbé about "anti-computer style" chess
• A guide to Endgame Tablebases
• GameDev.net – Chess Programming by François-Dominic Laramée Part 1 2 3 4 5 6
• Colin Frayn's Computer Chess Theory Page
• ""How REBEL Plays Chess" by Ed Schröder" (PDF). (268 KB)
• "Play chess with God" – Play chess against Ken Thompson's endgame database
• Chess programming wiki
• Computer Chess Club Forums
• The Strongest Computer Chess Engines Over Time